{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An overview of supervised classification techniques with case study :\n",
    "## Predicting liver disease from data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "We will go through the entire data pipeline from data cleaning, preprocessing, staging and modelling on a relatively small dataset (~600 entries). On our way, we will compare different machine learning models and try to measure their performance using various metrics.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "The dataset for this problem is the ILPD (Indian Liver Patient Dataset) taken from the UCI Machine Learning Repository (URL https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset) ). Number of instances are 583. It is a multivariate data set, contain 10 variables that are age, gender, total Bilirubin, direct Bilirubin, total proteins, albumin, A/G ratio, SGPT, SGOT and Alkphos. All values are real integers. This data set contains 416 liver patient records and 167 non liver patient records.The data set was collected from north east of Andhra Pradesh, India. Selector is a class label used to divide into groups(liver patient or not). This data set contains 441 male patient records and 142 female patient records. Any patient whose age exceeded 89 is listed as being of age \"90\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Reading Data\n",
    "Below, we will load the ILPD data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of samples: 582. No of features in each sample: 11 .\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>65</th>\n",
       "      <th>Female</th>\n",
       "      <th>0.7</th>\n",
       "      <th>0.1</th>\n",
       "      <th>187</th>\n",
       "      <th>16</th>\n",
       "      <th>18</th>\n",
       "      <th>6.8</th>\n",
       "      <th>3.3</th>\n",
       "      <th>0.9</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>208</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   65 Female   0.7  0.1  187  16   18  6.8  3.3   0.9  1\n",
       "0  62   Male  10.9  5.5  699  64  100  7.5  3.2  0.74  1\n",
       "1  62   Male   7.3  4.1  490  60   68  7.0  3.3  0.89  1\n",
       "2  58   Male   1.0  0.4  182  14   20  6.8  3.4  1.00  1\n",
       "3  72   Male   3.9  2.0  195  27   59  7.3  2.4  0.40  1\n",
       "4  46   Male   1.8  0.7  208  19   14  7.6  4.4  1.30  1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will need following libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Loading the ILPD dataset\n",
    "df = pd.read_csv(\"ILPD.csv\")\n",
    "\n",
    "print (f\"No of samples: {df.shape[0]}. No of features in each sample: {df.shape[1]} .\")\n",
    "# Display the first  5 records\n",
    "df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " As you can see above, the raw dataset does not have any column headings. So, we will manually add the headings taken from the URL mentioned above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "headings = ['Age','Gender','Total Bilirubin','Direct Bilirubin','Total Proteins','Albumin',\n",
    "            'A/G ratio','SGPT','SGOT','Alkphos','Disease']\n",
    "df = pd.read_csv(\"ILPD.csv\", names = headings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total Bilirubin</th>\n",
       "      <th>Direct Bilirubin</th>\n",
       "      <th>Total Proteins</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>A/G ratio</th>\n",
       "      <th>SGPT</th>\n",
       "      <th>SGOT</th>\n",
       "      <th>Alkphos</th>\n",
       "      <th>Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Total Bilirubin  Direct Bilirubin  Total Proteins  Albumin  \\\n",
       "0   65  Female              0.7               0.1             187       16   \n",
       "1   62    Male             10.9               5.5             699       64   \n",
       "2   62    Male              7.3               4.1             490       60   \n",
       "3   58    Male              1.0               0.4             182       14   \n",
       "4   72    Male              3.9               2.0             195       27   \n",
       "\n",
       "   A/G ratio  SGPT  SGOT  Alkphos  Disease  \n",
       "0         18   6.8   3.3     0.90        1  \n",
       "1        100   7.5   3.2     0.74        1  \n",
       "2         68   7.0   3.3     0.89        1  \n",
       "3         20   6.8   3.4     1.00        1  \n",
       "4         59   7.3   2.4     0.40        1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The last column - 'Disease', contains the labels\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Preprocessing Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Removing duplicates\n",
    "Duplicates rows can be present in data due to various reasons. The question about whether duplicates should be removed or not depends on the particular problem setting and context. For example - In linear regression, if you have duplicate rows, then the underlying assumption about standard error terms being uncorrelated will be violated, which will lead our model to underestimate the actual standard errors. Thus, duplicates must be removed in that case.\n",
    "\n",
    "In our case, removing duplicates makes sense, as most likely it is possible that someone entered the data for a patient multiple times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(583, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape # rows, columns of original dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total Bilirubin</th>\n",
       "      <th>Direct Bilirubin</th>\n",
       "      <th>Total Proteins</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>A/G ratio</th>\n",
       "      <th>SGPT</th>\n",
       "      <th>SGOT</th>\n",
       "      <th>Alkphos</th>\n",
       "      <th>Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>40</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>293</td>\n",
       "      <td>232</td>\n",
       "      <td>245</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>293</td>\n",
       "      <td>232</td>\n",
       "      <td>245</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289</td>\n",
       "      <td>875</td>\n",
       "      <td>731</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289</td>\n",
       "      <td>875</td>\n",
       "      <td>731</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>38</td>\n",
       "      <td>Female</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>410</td>\n",
       "      <td>59</td>\n",
       "      <td>57</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>38</td>\n",
       "      <td>Female</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>410</td>\n",
       "      <td>59</td>\n",
       "      <td>57</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>8.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>272</td>\n",
       "      <td>31</td>\n",
       "      <td>61</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>8.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>272</td>\n",
       "      <td>31</td>\n",
       "      <td>61</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>158</td>\n",
       "      <td>37</td>\n",
       "      <td>43</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>158</td>\n",
       "      <td>37</td>\n",
       "      <td>43</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>145</td>\n",
       "      <td>32</td>\n",
       "      <td>92</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>145</td>\n",
       "      <td>32</td>\n",
       "      <td>92</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>158</td>\n",
       "      <td>29</td>\n",
       "      <td>39</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>158</td>\n",
       "      <td>29</td>\n",
       "      <td>39</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>282</td>\n",
       "      <td>72</td>\n",
       "      <td>140</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>282</td>\n",
       "      <td>72</td>\n",
       "      <td>140</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>30</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>332</td>\n",
       "      <td>84</td>\n",
       "      <td>139</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>30</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>332</td>\n",
       "      <td>84</td>\n",
       "      <td>139</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>196</td>\n",
       "      <td>20</td>\n",
       "      <td>35</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>196</td>\n",
       "      <td>20</td>\n",
       "      <td>35</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>39</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>180</td>\n",
       "      <td>42</td>\n",
       "      <td>62</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>39</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>180</td>\n",
       "      <td>42</td>\n",
       "      <td>62</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>175</td>\n",
       "      <td>48</td>\n",
       "      <td>34</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>175</td>\n",
       "      <td>48</td>\n",
       "      <td>34</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>218</td>\n",
       "      <td>50</td>\n",
       "      <td>53</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>218</td>\n",
       "      <td>50</td>\n",
       "      <td>53</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  Total Bilirubin  Direct Bilirubin  Total Proteins  Albumin  \\\n",
       "18    40  Female              0.9               0.3             293      232   \n",
       "19    40  Female              0.9               0.3             293      232   \n",
       "25    34    Male              4.1               2.0             289      875   \n",
       "26    34    Male              4.1               2.0             289      875   \n",
       "33    38  Female              2.6               1.2             410       59   \n",
       "34    38  Female              2.6               1.2             410       59   \n",
       "54    42    Male              8.9               4.5             272       31   \n",
       "55    42    Male              8.9               4.5             272       31   \n",
       "61    58    Male              1.0               0.5             158       37   \n",
       "62    58    Male              1.0               0.5             158       37   \n",
       "105   36    Male              5.3               2.3             145       32   \n",
       "106   36    Male              5.3               2.3             145       32   \n",
       "107   36    Male              0.8               0.2             158       29   \n",
       "108   36    Male              0.8               0.2             158       29   \n",
       "137   18    Male              0.8               0.2             282       72   \n",
       "138   18    Male              0.8               0.2             282       72   \n",
       "142   30    Male              1.6               0.4             332       84   \n",
       "143   30    Male              1.6               0.4             332       84   \n",
       "157   72    Male              0.7               0.1             196       20   \n",
       "158   72    Male              0.7               0.1             196       20   \n",
       "163   39    Male              1.9               0.9             180       42   \n",
       "164   39    Male              1.9               0.9             180       42   \n",
       "173   31    Male              0.6               0.1             175       48   \n",
       "174   31    Male              0.6               0.1             175       48   \n",
       "200   49    Male              0.6               0.1             218       50   \n",
       "201   49    Male              0.6               0.1             218       50   \n",
       "\n",
       "     A/G ratio  SGPT  SGOT  Alkphos  Disease  \n",
       "18         245   6.8   3.1     0.80        1  \n",
       "19         245   6.8   3.1     0.80        1  \n",
       "25         731   5.0   2.7     1.10        1  \n",
       "26         731   5.0   2.7     1.10        1  \n",
       "33          57   5.6   3.0     0.80        2  \n",
       "34          57   5.6   3.0     0.80        2  \n",
       "54          61   5.8   2.0     0.50        1  \n",
       "55          61   5.8   2.0     0.50        1  \n",
       "61          43   7.2   3.6     1.00        1  \n",
       "62          43   7.2   3.6     1.00        1  \n",
       "105         92   5.1   2.6     1.00        2  \n",
       "106         92   5.1   2.6     1.00        2  \n",
       "107         39   6.0   2.2     0.50        2  \n",
       "108         39   6.0   2.2     0.50        2  \n",
       "137        140   5.5   2.5     0.80        1  \n",
       "138        140   5.5   2.5     0.80        1  \n",
       "142        139   5.6   2.7     0.90        1  \n",
       "143        139   5.6   2.7     0.90        1  \n",
       "157         35   5.8   2.0     0.50        1  \n",
       "158         35   5.8   2.0     0.50        1  \n",
       "163         62   7.4   4.3     1.38        1  \n",
       "164         62   7.4   4.3     1.38        1  \n",
       "173         34   6.0   3.7     1.60        1  \n",
       "174         34   6.0   3.7     1.60        1  \n",
       "200         53   5.0   2.4     0.90        1  \n",
       "201         53   5.0   2.4     0.90        1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_duplicate = df[df.duplicated(keep = False)] # keep = False gives you all rows with duplicate entries\n",
    "df_duplicate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(570, 11)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[~df.duplicated(subset = None, keep = 'first')]\n",
    "# Here, keep = 'first' ensures that only the first row is taken into the final dataset.\n",
    "# The '~' sign tells pandas to keep all values except the 13 duplicate values\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Dealing with null values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will remove null values (or perform some kind of imputation) before running models on the data. We can use Imputer from sklearn.preprocessing or fillna() for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age                 0\n",
      "Gender              0\n",
      "Total Bilirubin     0\n",
      "Direct Bilirubin    0\n",
      "Total Proteins      0\n",
      "Albumin             0\n",
      "A/G ratio           0\n",
      "SGPT                0\n",
      "SGOT                0\n",
      "Alkphos             4\n",
      "Disease             0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum()) # display number of null values by column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length before removing NaN values:570\n",
      "length after removing NaN values:566\n"
     ]
    }
   ],
   "source": [
    "# We can see that the column 'Alkphos' has 4 missing values\n",
    "# One way to deal with them can be to just directly remove these 4 values\n",
    "print (\"length before removing NaN values:%d\"%len(df))\n",
    "df2 = df[pd.notnull(df['Alkphos'])]\n",
    "print (\"length after removing NaN values:%d\"%len(df2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data imputation of missing values with a certain order statistic(mean, mode, median) can be done if sufficient context is available. For example - In our case, let us print those rows whose values of column 'Alkphos' are missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total Bilirubin</th>\n",
       "      <th>Direct Bilirubin</th>\n",
       "      <th>Total Proteins</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>A/G ratio</th>\n",
       "      <th>SGPT</th>\n",
       "      <th>SGOT</th>\n",
       "      <th>Alkphos</th>\n",
       "      <th>Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>45</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>189</td>\n",
       "      <td>23</td>\n",
       "      <td>33</td>\n",
       "      <td>6.6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>230</td>\n",
       "      <td>24</td>\n",
       "      <td>46</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>35</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>180</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>27</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>106</td>\n",
       "      <td>25</td>\n",
       "      <td>54</td>\n",
       "      <td>8.5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  Total Bilirubin  Direct Bilirubin  Total Proteins  Albumin  \\\n",
       "209   45  Female              0.9               0.3             189       23   \n",
       "241   51    Male              0.8               0.2             230       24   \n",
       "253   35  Female              0.6               0.2             180       12   \n",
       "312   27    Male              1.3               0.6             106       25   \n",
       "\n",
       "     A/G ratio  SGPT  SGOT  Alkphos  Disease  \n",
       "209         33   6.6   3.9      NaN        1  \n",
       "241         46   6.5   3.1      NaN        1  \n",
       "253         15   5.2   2.7      NaN        2  \n",
       "312         54   8.5   4.8      NaN        2  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[~pd.notnull(df['Alkphos'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no homogeneity among patients at least in terms of age and gender, so it may be more difficult to impute a value in this case. Right now, just to show how imputation is done, we will substitute these values with the mode of 'Alkphos'. \n",
    "Later on, we can check the importance of the variable 'Alkphos' to see whether this column is significant enough to warrant an effort on trying different imputations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9 , 0.74, 0.89, 1.  , 0.4 , 1.3 , 1.1 , 1.2 , 0.8 , 0.6 , 0.87,\n",
       "       0.7 , 0.92, 0.55, 0.5 , 1.85, 0.95, 1.4 , 1.18, 0.61, 1.34, 1.39,\n",
       "       1.6 , 1.58, 1.25, 0.78, 0.76, 1.55, 0.71, 0.62, 0.67, 0.75, 1.16,\n",
       "       1.5 , 1.66, 0.96, 1.38, 0.52, 0.47, 0.93, 0.48, 0.58, 0.69, 1.27,\n",
       "       1.12, 1.06, 0.53, 1.03, 0.68, 1.9 , 1.7 , 1.8 , 0.3 , 0.97, 0.35,\n",
       "       1.51, 0.64, 0.45, 1.36, 0.88, 1.09, 1.11, 1.72, 2.8 , 0.46, 0.39,\n",
       "       1.02, 2.5 , 0.37])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Alkphos'] = df['Alkphos'].fillna(df['Alkphos'].mode().iloc[0])\n",
    "df['Alkphos'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.00    108\n",
       "0.80     62\n",
       "0.90     57\n",
       "0.70     53\n",
       "1.10     45\n",
       "1.20     35\n",
       "0.60     31\n",
       "0.50     26\n",
       "1.30     25\n",
       "1.40     17\n",
       "0.40     14\n",
       "1.50     10\n",
       "1.60      4\n",
       "1.70      4\n",
       "0.30      4\n",
       "0.75      4\n",
       "0.96      3\n",
       "1.80      3\n",
       "0.92      2\n",
       "1.16      2\n",
       "0.52      2\n",
       "0.47      2\n",
       "0.76      2\n",
       "0.93      2\n",
       "1.38      2\n",
       "2.50      2\n",
       "1.85      2\n",
       "1.34      2\n",
       "0.95      2\n",
       "1.06      2\n",
       "       ... \n",
       "0.48      1\n",
       "0.62      1\n",
       "1.12      1\n",
       "1.03      1\n",
       "0.53      1\n",
       "0.55      1\n",
       "0.46      1\n",
       "0.69      1\n",
       "1.25      1\n",
       "1.66      1\n",
       "1.55      1\n",
       "0.61      1\n",
       "0.39      1\n",
       "0.64      1\n",
       "0.78      1\n",
       "0.37      1\n",
       "1.27      1\n",
       "0.68      1\n",
       "0.67      1\n",
       "0.58      1\n",
       "1.11      1\n",
       "0.71      1\n",
       "1.72      1\n",
       "0.35      1\n",
       "0.45      1\n",
       "0.88      1\n",
       "1.02      1\n",
       "1.09      1\n",
       "0.89      1\n",
       "1.36      1\n",
       "Name: Alkphos, Length: 69, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Alkphos'].value_counts()# 1 actually occurs twice as more as the next frequent value, maybe this should give some\n",
    "#justification as to why the mode may be appropriate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.96\n",
      "0.9483684210526314\n"
     ]
    }
   ],
   "source": [
    "print(df['Alkphos'].median()) # To check how different are mean and median from the mode in this particular case\n",
    "print(df['Alkphos'].mean()) # Use mean only if there are no outliers in your data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On a closing note, one can also predict the missing values by using the portion of the dataset that has non-NaN entries for that column as a training set. This methods is not ideal however, if there is no correlation between the entries with missing values and the other entries, as this method will introduce an unnecessary bias in its predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Outlier Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even before going to actual algorithms, we can do some outlier detection by applying filters to our data. Ex- We know that age cannot be negative.\n",
    "\n",
    "Here we will first use Isolation Forest, which is similar to Random Forest, for outlier detection. The basic premise behind this method is that an outlier is identified much quickly (i.e., in fewer splits) of a decision tree, compared to a normal data point. Thus the anomaly score for a point is directly proportional to the average of path lengths to isolate that point over several decision trees.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/app/venv/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import IsolationForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = IsolationForest(max_samples=100, contamination = 0.01, random_state=7)\n",
    "# contamination indicates the percentage of outliers in the dataset (Key hyperparameter!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Running outlier detection on column 'Albumin', A/G ratio'\n",
    "clf.fit(np.vstack((df['Albumin'].values, df['A/G ratio'].values)).T)# converting to np array and then passing as a 2-d array\n",
    "clf_pred = clf.predict(np.vstack((df['Albumin'].values, df['A/G ratio'].values)).T)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1, -1, -1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_pred # - 1 means outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = []\n",
    "for i in clf_pred:\n",
    "    if i == 1: colors.append('b')\n",
    "    else: colors.append('r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f15e09b7dd8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Albumin'].values, df['A/G ratio'].values, color = colors)# outliers are colored red"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly one can run Isolation Forest on other pairs of columns and then manually inspect the outlier points.\n",
    "\n",
    "Another method that can be used from sklearn is covariance.EllipticEnvelope, which assumes that the data given to it has a Gaussian distribution and thus fits ellipsoids on it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Log transform certain features\n",
    "After removing the column ‘Disease’ from the dataset as it is the label, we display all features in a histogram format to check if any feature has a skewed distribution. On those features, a log transformation is applied to reduce their range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f15e0914a58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15e08de0b8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15e09055f8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f15e08aeb38>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15e085d0f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15e0885668>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f15e082cbe0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15e07de1d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15e07de208>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split the data into features and target label(disease)\n",
    "disease_initial = df['Disease']\n",
    "features_initial = df.drop('Disease', axis = 1)\n",
    "\n",
    "# Visualize skewed continuous features of original data\n",
    "features_initial.hist(figsize=(14,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f161d73d080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f161c48f630>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f161c4b2ba8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f161c45f160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f161c4066d8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f161c42fc50>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f161c3e1208>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f161c3857b8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f161c3857f0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f161c35f2b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f161c306828>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f161c32dda0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Skewed features are Albumin, Direct Bilirubin, A/G ratio, Tota Bilirubin, Total Protein \n",
    "#Log-transform the skewed features\n",
    "skewed = ['Albumin', 'Direct Bilirubin', 'Total Bilirubin', 'A/G ratio', 'Total Proteins']\n",
    "features_initial[skewed] = df[skewed].apply(lambda x: np.log(x + 1))\n",
    "df[skewed] = features_initial[skewed]\n",
    "# Visualize the new log distributions\n",
    "df.hist(figsize=(14,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, if we need certain certain features to have a normal distribution, we can do that by using the PowerTransformer method in sklearn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Scaling of features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardization (subtracting mean and scaling variance) is required for many methods like regularized linear regression, K-NearestNeighbors. One can use following scaling methods - \n",
    "\n",
    "1. MinMaxScaler - It preserves zero entries if the feature matrix is sparse, and is also robust to small values of S.D. for features.\n",
    "\n",
    "2. StandardScaler - Center around zero, change variance to 1.\n",
    "\n",
    "3. RobustScaler - Useful if there are a lot of outliers in data, this method uses percentiles to center and scale data.\n",
    "\n",
    "Normalizing, on the other hand, causes one row of the input (one sample) to have unit norm. This is useful for Gram matrix entries in kernel methods.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use MinMaxScaler here as StandardScaler gives very low values, with some in the order of 10^-16, which might be difficult to relate to and visualize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Total Bilirubin</th>\n",
       "      <th>Direct Bilirubin</th>\n",
       "      <th>Total Proteins</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>A/G ratio</th>\n",
       "      <th>SGPT</th>\n",
       "      <th>SGOT</th>\n",
       "      <th>Alkphos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>570.000000</td>\n",
       "      <td>570.000000</td>\n",
       "      <td>570.000000</td>\n",
       "      <td>570.000000</td>\n",
       "      <td>570.000000</td>\n",
       "      <td>570.000000</td>\n",
       "      <td>570.000000</td>\n",
       "      <td>570.000000</td>\n",
       "      <td>570.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.474990</td>\n",
       "      <td>0.179828</td>\n",
       "      <td>0.175155</td>\n",
       "      <td>0.383482</td>\n",
       "      <td>0.264557</td>\n",
       "      <td>0.258082</td>\n",
       "      <td>0.550191</td>\n",
       "      <td>0.488902</td>\n",
       "      <td>0.259347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.188863</td>\n",
       "      <td>0.185859</td>\n",
       "      <td>0.224849</td>\n",
       "      <td>0.151618</td>\n",
       "      <td>0.169167</td>\n",
       "      <td>0.160818</td>\n",
       "      <td>0.157725</td>\n",
       "      <td>0.173220</td>\n",
       "      <td>0.127416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.337209</td>\n",
       "      <td>0.062919</td>\n",
       "      <td>0.029648</td>\n",
       "      <td>0.290977</td>\n",
       "      <td>0.149929</td>\n",
       "      <td>0.140897</td>\n",
       "      <td>0.449275</td>\n",
       "      <td>0.369565</td>\n",
       "      <td>0.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.476744</td>\n",
       "      <td>0.089297</td>\n",
       "      <td>0.056921</td>\n",
       "      <td>0.338512</td>\n",
       "      <td>0.227851</td>\n",
       "      <td>0.219448</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.264000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.627907</td>\n",
       "      <td>0.236455</td>\n",
       "      <td>0.251327</td>\n",
       "      <td>0.440946</td>\n",
       "      <td>0.329197</td>\n",
       "      <td>0.340134</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.630435</td>\n",
       "      <td>0.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  Total Bilirubin  Direct Bilirubin  Total Proteins  \\\n",
       "count  570.000000       570.000000        570.000000      570.000000   \n",
       "mean     0.474990         0.179828          0.175155        0.383482   \n",
       "std      0.188863         0.185859          0.224849        0.151618   \n",
       "min      0.000000         0.000000          0.000000        0.000000   \n",
       "25%      0.337209         0.062919          0.029648        0.290977   \n",
       "50%      0.476744         0.089297          0.056921        0.338512   \n",
       "75%      0.627907         0.236455          0.251327        0.440946   \n",
       "max      1.000000         1.000000          1.000000        1.000000   \n",
       "\n",
       "          Albumin   A/G ratio        SGPT        SGOT     Alkphos  \n",
       "count  570.000000  570.000000  570.000000  570.000000  570.000000  \n",
       "mean     0.264557    0.258082    0.550191    0.488902    0.259347  \n",
       "std      0.169167    0.160818    0.157725    0.173220    0.127416  \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "25%      0.149929    0.140897    0.449275    0.369565    0.160000  \n",
       "50%      0.227851    0.219448    0.565217    0.478261    0.264000  \n",
       "75%      0.329197    0.340134    0.652174    0.630435    0.320000  \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Initialize a scaler, then apply it to the features\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "normalized = ['Age', 'SGPT', 'SGOT', 'Alkphos', 'Albumin', 'Direct Bilirubin', 'Total Bilirubin', 'A/G ratio', 'Total Proteins']\n",
    "skewed = ['Albumin', 'Direct Bilirubin', 'Total Bilirubin', 'A/G ratio', 'Total Proteins']\n",
    "features_initial[normalized] = scaler.fit_transform(df[normalized])\n",
    "\n",
    "# Show an example of a record with scaling applied\n",
    "display(features_initial.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Dealing with categorical features\n",
    "One-hot encoding is a popular method that can be used when the different values of features do not have any inherent ordering.\n",
    "If we know of some ordering (such as a feature which has possible values 'Small', 'Medium', 'Large'), then using the method LabelEncoder can make more sense.\n",
    "Here we use pd.get_dummies() method to one-hot encode the feature ‘Gender’ as well as the label ‘Disease’ ( with the integer ‘1’ representing presence of disease).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 total features after one-hot encoding.\n",
      "['Age', 'Total Bilirubin', 'Direct Bilirubin', 'Total Proteins', 'Albumin', 'A/G ratio', 'SGPT', 'SGOT', 'Alkphos', 'Gender_Female', 'Gender_Male']\n",
      "2 disease columns after one-hot encoding.\n"
     ]
    }
   ],
   "source": [
    "# TODO: One-hot encode the data using pandas.get_dummies()\n",
    "features = pd.get_dummies(features_initial)\n",
    "\n",
    "encoded = list(features.columns)\n",
    "print (\"{} total features after one-hot encoding.\".format(len(encoded)))\n",
    "print (encoded)\n",
    "\n",
    "disease=pd.get_dummies(disease_initial)\n",
    "encoded = list(disease.columns)\n",
    "print (\"{} disease columns after one-hot encoding.\".format(len(encoded)))\n",
    "#print disease[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 456 samples.\n",
      "Testing set has 114 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/app/venv/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Import train_test_split\n",
    "from sklearn.cross_validation import train_test_split, ShuffleSplit\n",
    "\n",
    "# Split the 'features' and 'income' data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, disease[1], test_size = 0.2, random_state = 7)\n",
    "\n",
    "# Show the results of the split\n",
    "print (\"Training set has {} samples.\".format(X_train.shape[0]))\n",
    "print (\"Testing set has {} samples.\".format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling\n",
    "\n",
    "### 1. Baseline Predictor\n",
    "\n",
    "Before applying any supervised learning technique, we will implement a naïve predictor, that will simply return that every data point has ‘Disease’= True. We will check our metrics(accuracy, TPR, FPR) on that predictor.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "disease = disease[:][1] # disease means '1', healthy means '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "      False       1.00      1.00      1.00       164\n",
      "       True       1.00      1.00      1.00       406\n",
      "\n",
      "avg / total       1.00      1.00      1.00       570\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "#naive accuracy, assuming that we predict everyone has disease\n",
    "positive_disease= (df['Disease'] == 1)\n",
    "positive_disease.astype(int) # convert boolean array to float\n",
    "report = classification_report(positive_disease, disease)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00       129\n",
      "          1       1.00      1.00      1.00       327\n",
      "\n",
      "avg / total       1.00      1.00      1.00       456\n",
      "\n",
      "Accuracy on training set:  1.0\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.37      0.33      0.35        39\n",
      "          1       0.67      0.71      0.69        75\n",
      "\n",
      "avg / total       0.57      0.58      0.57       114\n",
      "\n",
      "Accuracy on test set:  0.5789473684210527\n"
     ]
    }
   ],
   "source": [
    "y_train_preds = clf.predict(X_train)\n",
    "print(classification_report(y_train_preds, y_train))\n",
    "print(\"Accuracy on training set: \", accuracy_score(y_train_preds, y_train))\n",
    "y_test_preds = clf.predict(X_test)\n",
    "print(classification_report(y_test_preds, y_test))\n",
    "print(\"Accuracy on test set: \", accuracy_score(y_test_preds, y_test)) # The tree is overfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing the decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pydotplus in /srv/app/venv/lib/python3.6/site-packages\r\n",
      "Requirement already satisfied: pyparsing>=2.0.1 in /srv/app/venv/lib/python3.6/site-packages (from pydotplus)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16,5))\n",
    "importances = clf.feature_importances_\n",
    "x= [i for i in range(11)]\n",
    "plt.xticks(x, list(features.columns))\n",
    "plt.bar(x, importances)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# USE THIS CODE TO GET VISUALIZATION OF TREE STRUCTURE USING GRAPHVIZ (YOU NEED TO HAVE GRAPHVIZ INSTALLED FOR THIS TO WORK)\n",
    "\n",
    "# import graphviz\n",
    "# import os\n",
    "# os.environ[\"PATH\"] += os.pathsep + # enter folder where graphviz is installed\n",
    "\n",
    "# from sklearn.externals.six import StringIO  \n",
    "# from IPython.display import Image  \n",
    "# from sklearn.tree import export_graphviz\n",
    "# import pydotplus\n",
    "# dot_data = StringIO()\n",
    "# export_graphviz(tree, out_file=dot_data,  \n",
    "#                 filled=True, rounded=True,\n",
    "#                 special_characters=True)\n",
    "# graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "# Image(graph.create_png())\n",
    "\n",
    "# !dot -Tpng tree.dot > output.png\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import fbeta_score, accuracy_score\n",
    "def train_predict(learner, sample_size, X_train, y_train, X_test, y_test): \n",
    "    results = {}\n",
    "\n",
    "    start = time() # Get start time\n",
    "    learner.fit(X_train[:sample_size], y_train[:sample_size])\n",
    "    end = time() # Get end time\n",
    "    \n",
    "    results['train_time'] = end - start\n",
    "        \n",
    "    start = time() \n",
    "    predictions_test = learner.predict(X_test)# predictions on test set\n",
    "    predictions_train = learner.predict(X_train[:200])# predictions on first 200 elements of training set\n",
    "    end = time()\n",
    "    \n",
    "    results['pred_time'] = end - start\n",
    "            \n",
    "    results['acc_train'] = accuracy_score(y_train[:200],predictions_train)\n",
    "        \n",
    "    results['acc_test'] = accuracy_score(y_test,predictions_test)\n",
    "    \n",
    "    results['f_train'] = fbeta_score(y_train[:200],predictions_train,beta=2)\n",
    "        \n",
    "    results['f_test'] = fbeta_score(y_test,predictions_test,beta=2)\n",
    "       \n",
    "    print (\"{} trained on {} samples.\".format(learner.__class__.__name__, sample_size))\n",
    "        \n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supervised Learning Approach\n",
    "Thereafter, we will use these supervised learning approaches for this problem:\n",
    "1. Logistic Classification: Since the outcome is binary and we have a reasonable number of examples at our disposal compared to number of features, this approach seems suitable. At the core of this method is a logistic or sigmoid function that quantifies the difference between each prediction and its corresponding true value.                                                                  \n",
    "2. Support Vector Machine:   SVM aims to find an optimal hyperplane that separates the data into different classes.                                  \n",
    "3. Random Forest Classifier: It comes under the category of ensemble methods. It employs ‘bagging’ method to draw a random subset from the data, and train a Decision Tree on that.                                                           \n",
    "\n",
    "4. k Nearest Neighbors: It uses the k closest neighbors of a data point to determine which class should that data point belong to.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "# TODO: Initialize the three models\n",
    "\n",
    "clf_base = LogisticRegression(random_state=7)\n",
    "clf_A = RandomForestClassifier(random_state=7)\n",
    "clf_B = SVC(random_state=7)\n",
    "clf_C = KNeighborsClassifier()\n",
    "\n",
    "# TODO: Calculate the number of samples for 20%, 50%, and 100% of the training data\n",
    "samples_20 = int(len(X_train) * 0.2)\n",
    "samples_50 = int(len(X_train) * 0.5)\n",
    "samples_100 = int(len(X_train) )\n",
    "result_1={}\n",
    "result_1=train_predict(clf_base, samples, X_train, y_train, X_test, y_test)\n",
    "print ('Performance metrics for benchmark model (Logistic regression):')\n",
    "print ('Accuracy score on training subset:%.2f'%result_1['acc_train'])\n",
    "print ('Accuracy score on test subset:%.2f'%result_1['acc_test'])\n",
    "print ('F-score on training subset:%.2f'%result_1['f_train'])\n",
    "print ('F-score on test subset:%.2f'%result_1['f_test'])\n",
    "# Collect results on the learners\n",
    "results = {}\n",
    "for clf in [clf_A, clf_B, clf_C]:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    results[clf_name] = {}\n",
    "    for i, samples in enumerate([samples_20, samples_50, samples_100]):\n",
    "        results[clf_name][i] = train_predict(clf, samples, X_train, y_train, X_test, y_test)\n",
    "\n",
    "#vs.evaluate(results, accuracy, fscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC Curve\n",
    "\n",
    "An additional metric called as Receiver Operator Characteristics(ROC) curve will be used. It plots the curve of True Positive Rate vs the False positive Rate, with a greater area under the curve indicating a better True Positive Rate for the same False Positive Rate. This can be helpful in this case as simply knowing the number of correct predictions may not suffice.\n",
    "  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For classifier RandomForestClassifier, ROC score is 0.659091\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAicAAAGHCAYAAABrpPKuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCCGEEJtCxQkhhBBCbMr/Aefymytw/DuMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12568550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For classifier SVC, ROC score is 0.500000\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAicAAAGHCAYAAABrpPKuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCCFG5f8AZ1h1i/R5ANcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1236c668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For classifier KNeighborsClassifier, ROC score is 0.523539\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAicAAAGHCAYAAABrpPKuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJUMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf6a5240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting ROC curve\n",
    "\n",
    "for clf in [clf_A, clf_B, clf_C]:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    pred=clf.predict(X_test)\n",
    "    fpr, tpr, _ = roc_curve(y_test, pred)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    print \"For classifier %s, ROC score is %f\"%(clf_name,roc_auc)\n",
    "    plt.figure()\n",
    "    lw = 2\n",
    "    plt.plot(fpr, tpr, color='darkorange',lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "#fpr, tpr, thresholds = roc_curve(y, scores, pos_label=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fine Tuning the Models\n",
    "Now we will use the grid search method to see if we can improve the performance of various models with different values for the given hyperparameters. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For classifier RandomForestClassifier:\n",
      "Unoptimized model\n",
      "------\n",
      "Accuracy score on testing data: 0.7414\n",
      "F-score on testing data: 0.8219\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 0.7500\n",
      "Final F-score on the testing data: 0.8686\n",
      "Best parameters:\n",
      "{'max_features': None, 'n_estimators': 15, 'oob_score': False, 'max_depth': 3}\n",
      "For classifier RandomForestClassifier, ROC score is 0.603896\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAicAAAGHCAYAAABrpPKuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf73a278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------------X----------------------------\n",
      "\n",
      "For classifier SVC:\n",
      "Unoptimized model\n",
      "------\n",
      "Accuracy score on testing data: 0.7586\n",
      "F-score on testing data: 0.9402\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 0.7586\n",
      "Final F-score on the testing data: 0.9402\n",
      "Best parameters:\n",
      "{'kernel': 'poly', 'C': 0.001}\n",
      "For classifier SVC, ROC score is 0.500000\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAicAAAGHCAYAAABrpPKuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqEhyIoQQQoig8v9Rj2mZpoUMvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf751710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------------X----------------------------\n",
      "\n",
      "For classifier KNeighborsClassifier:\n",
      "Unoptimized model\n",
      "------\n",
      "Accuracy score on testing data: 0.6466\n",
      "F-score on testing data: 0.7631\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 0.6552\n",
      "Final F-score on the testing data: 0.7960\n",
      "Best parameters:\n",
      "{'n_neighbors': 15, 'weights': 'uniform'}\n",
      "For classifier KNeighborsClassifier, ROC score is 0.492695\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAicAAAGHCAYAAABrpPKuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEUIIIYRX+X8rWa4VypXxQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12358400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------------X----------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import roc_curve,auc\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.metrics import make_scorer\n",
    "# TODO: Initialize the classifier\n",
    "i=0\n",
    "for clf in [clf_A, clf_B, clf_C]:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    print 'For classifier %s:'%(clf_name)\n",
    "    if i==0: #RandomForest\n",
    "        parameters = {'max_features':['auto',None],# 'auto' option uses square root of number of features\n",
    "                     'oob_score':[False,True],# setting it to 'True' saves the generalization error\n",
    "                     'max_depth':[3,10,15],# depth of tree\n",
    "                     'n_estimators':[3,10,15]}# number of trees\n",
    "    elif i==1: #SVM\n",
    "        parameters={'kernel':['poly','rbf','linear'],# different ways to separate data pts by a hyperplane\n",
    "                    'C':[0.001,1,1000]} # weight of penalty assigned to error\n",
    "    elif i==2: #kNearestClassifier\n",
    "        parameters={'n_neighbors':[5,10,15],# number of neighbors\n",
    "                    'weights':['uniform','distance']}# distance means weights are inversely proportional to distance\n",
    "\n",
    "    scorer = make_scorer(fbeta_score, beta=2)\n",
    "    grid_obj = GridSearchCV(clf,parameters,scoring=scorer)\n",
    "    grid_fit = grid_obj.fit(X_train,y_train)\n",
    "    best_clf = grid_fit.best_estimator_\n",
    "\n",
    "    predictions = (clf.fit(X_train, y_train)).predict(X_test)\n",
    "    best_predictions = best_clf.predict(X_test)\n",
    "    # Report the before-and-afterscores\n",
    "    print \"Unoptimized model\\n------\"\n",
    "    print \"Accuracy score on testing data: {:.4f}\".format(accuracy_score(y_test, predictions))\n",
    "    print \"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, predictions, beta = 2))\n",
    "    print \"\\nOptimized Model\\n------\"\n",
    "    print \"Final accuracy score on the testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions))\n",
    "    print \"Final F-score on the testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 2))\n",
    "    print \"Best parameters:\"\n",
    "    print grid_fit.best_params_\n",
    "    fpr, tpr, _ = roc_curve(y_test, best_predictions)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    print \"For classifier %s, ROC score is %f\"%(clf_name,roc_auc)\n",
    "    plt.figure()\n",
    "    lw = 2\n",
    "    plt.plot(fpr, tpr, color='darkorange',lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "    print \"\\n---------------------------------X----------------------------\\n\"\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting important features\n",
    "From a medical perspective, it may be important to know which features are most influential in determining whether a person has a disease or not. Also, in future, it may be possible that more examples are collected for this dataset, and we may want to use only the k best features for this dataset for classification purpose. Keeping this in mind, we will use the RandomForestClassifier to extract the features of greatest importance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1QAAAHrCAYAAADIYtzUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAwCEaKgAAAABwiIYKAAAAABz6PxrxwJg+CXcDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc256be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf_new=clf_A.fit(X_train,y_train)#clf_A is RandomForestClassifier\n",
    "im_features = clf_new.feature_importances_\n",
    "vs.feature_plot(im_features, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model trained on full data\n",
      "------\n",
      "Accuracy on testing data: 0.7414\n",
      "F-score on testing data: 0.8333\n",
      "\n",
      "Final Model trained on reduced data\n",
      "------\n",
      "Accuracy on testing data: 0.6897\n",
      "F-score on testing data: 0.8053\n"
     ]
    }
   ],
   "source": [
    "from sklearn.base import clone\n",
    "\n",
    "X_train_reduced = X_train[X_train.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "X_test_reduced = X_test[X_test.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "\n",
    "clf = (clone(clf_A)).fit(X_train_reduced, y_train)#seeing how using only 5 best features affects RandomForestClassifier\n",
    "predictions=clf_A.predict(X_test)\n",
    "\n",
    "# Make new predictions\n",
    "reduced_predictions = clf.predict(X_test_reduced)\n",
    "\n",
    "# Report scores from Random Forest using both versions of data\n",
    "print \"Final Model trained on full data\\n------\"\n",
    "print \"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, predictions))\n",
    "print \"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, predictions, beta = 0.5))\n",
    "print \"\\nFinal Model trained on reduced data\\n------\"\n",
    "print \"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, reduced_predictions))\n",
    "print \"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, reduced_predictions, beta = 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results of feature reduction\n",
    "So,we see that 'Total Proteins' is the most important feature, although not by a significant margin. Using only 5 most important features leads to a reduction in accuracy by around 5% and F-score by around 3%. Maybe in future\n",
    "if number of data points is very large and we are willing to save some training time at the cost of accuracy, we can use this method.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion-Final Model Evaluation\n",
    "As the results show, it is difficult to arrive at a clear-cut answer for the best performing model among SVM and Random Forest. While SVM shows better result in terms of F-beta score (0.94), its area under the ROC curve is surprisingly low. Random Forest shows a decent enough F-beta score (0.87) and its area under ROC curve is 0.6. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
